There has been a rapid growth in the availability and retrieval of information through the World Wide Web. The web contains large volume of data that is stored in database data structures such as database tables or indexes or files. The data stored may be of different formats, different types, and may contain different domain-specific information.
Search engines such as Google™, Yahoo® etc are used to search through the large volume of data to retrieve results that are relevant to the user searching using the search engines. Most of these types of search engines provide set of results that are based on keywords used by the user in the search queries formulated for searching over the web.
Most of the users tend to search the information from the web that is relevant to the domain in which they are interested. So, while searching users normally use keywords and variants thereof to obtain relevant set of results. However, it is possible that the same keywords may have different meaning in different context and the results obtained as a result of search may not be relevant to the user-specific domain. Thus, information retrieval from large volume of data with different formats, types and context is a problematic and a tedious task.
Search engine frameworks play a key role in data mining for recruitment process. In a conventional online recruitment process, candidates seeking an employment submit their resumes to employer's job-portals or third-party's online job-boards that provides information about available vacancies and the eligibility criteria for the opening positions. Then the employers access these resumes to select the potential candidates based on skills, experience, qualifications of the candidates seeking request for employments in the employer's organization. However, this conventional process tends to be tedious, manual and time-consuming due to lack of uniformity in the data submitted by the job aspirants.
For a job aspirant employee interested in searching job, the task is to identify the potential employers looking for candidates that match her skills, to identify the exact number of openings and to monitor which employers are most likely to recruit etc. Thus, this approach is a trial and miss approach with the possibility of ending up with no identification of potential openings and interested job. The job aspirant may further end up spending manual efforts, money and time by repeatedly performing search over the search engines available until he is selected.
For an employer in order to identify the potential candidates, the task is to scan through the set of resumes uploaded on the employer's portal or obtained through different online job-boards. It requires herculean efforts and time to scan through the resume details and to evaluate the capabilities or skills of the candidates seeking request for employment. Further, resumes available may be in different file formats and may require assessing the terminology and formats to check the relevance of the candidate's domain and area of expertise. This can become a time consuming and tedious task. Consequently, the employer may end up overlooking potential or qualified candidates and end up selecting unqualified or comparatively inferior skilled candidates.
Thus, there is a need for efficient and intelligent search mechanism that searches relevant information from the large set of resumes available online or stored in an employer's job portal. Considering the example of resume searching over internet, there is a need for search mechanism that enables contextual search and identification of the resumes matching the user specified criteria. Efforts have been made in the past for implementing intelligent search mechanism for efficient searching and extracting data of interest to the user. Some of the inventions known to us are as follows:
The applications such as Daxtra, Resume Mirror applications use text extraction techniques to extract entities of interest from the resume database and store the extracted entities of interest in the xml files or databases.
U.S. Pat. No. 6,874,002 by Peleus, et al disclose a computer based system and method for creating a standardized or normalized resume format, extracting resume information from the normalized resume, and automatically inputting the resume information into a resume database.
U.S. Pat. No. 7,711,573 by Obeid disclose a method and system for managing a resume database wherein for each of the skill and experience related phrases in the resume, the said system computes the range of actual experience with context to the use of phrase in the resume. Further, the said experience computed is stored in the resume database which can be captured by the recruiter while finding resumes that satisfy the required job description.
U.S. Pat. No. 7,822,732 by Bodapati disclose a system that uses parameterized search scripts and configuration information along with user input search string to build search engine independent and search engine dependent queries from one or more search engines to provide relevant results to user.
U.S. Pat. No. 7,809,751 by Fuerst, et al disclose a method and apparatus for searching database data structure wherein the said database is populated with criteria relevant to specific domain interested to a user and generating search results using the said criteria.
US20020116391 by Nadkarni, Uday P disclose an online skill management system comprising a database containing fields related to employment criteria such as experience, skills, education etc wherein the prospective employer can access the system to compare the skill-sets of different individuals on the said database.
There are certain limitations that are observed in the above mentioned prior art that are as follows:                The results or hits retrieved using conventional search may not be relevant to the information in which the user is interested.        There is no feasibility for searching the resume by using derived attributes like total experience, skill-wise experience, domain-wise experience etc.        The additional domain intelligence in the form of searching resumes based on domain area, technology skills, roles and services are not supported.        The prior arts follow “Extract and then search approach”. This step of extraction takes significant time.        The information stored in the XML database or other relational databases needs to be searched using SQL queries and hence require significant amount of time in retrieving results.        The search on the known search engines is limited to search documents and files based on appearance of the keywords or terms in the content of the documents or files.        
Thus, the above discussed prior arts and the state of the art in general lack the relevancy in delivering resumes instantaneously to an employer interested in searching resumes of potential candidate on a local repository of resumes. Similarly, the results displayed to the user may not be always relevant to the user. This is because of lack of use of additional domain specific criteria by the employer for searching and retrieving resumes with exact relevance with-in domain in which he is interested.
Some of the prior arts do disclose using additional criteria such as domain-specific information for resume search. However, these criteria are stored in XML database and it takes more time for extracting such criteria from XML databases using SQL queries and hence is less efficient.
Also, the results obtained using the state of art resume search mechanism may not obtain the search results and rank them based on skill-wise experience, domain-wise experience, and total experience due to lack of computing these attributes form the content of the resumes. The state of art is only restricted to identifying additional criteria such as domain, skill, qualification etc and is unable to monitor or derive attributes related to these criteria. Deriving attributes related to the criteria such as domain, skill, designation, qualification etc from the resume content itself and using these attributes for searching may result in further enhancing the intelligent resume search mechanism.
In view of above, there is a need for a system and method for intelligent search mechanism that enables domain-specific search with improved efficiency and time required for retrieving content specific search results.